The results in tab:Kitti2015accuracy reveal, that the use of the *CT with a support region of \(9\times 7\)  pixels and its Hamming distance as a matching cost function, achieves the best results, in the evaluation of both the actual estimate and the interpolated version. As can be expected, the use of down-sampled versions of the input images yield less accurate results and yet, achieves a higher density in the resulting disparity maps. Furthermore, the use of the *NCC as a cost function achieves slightly less accurate results, while leading to a smaller throughput as discussed in sec:Experiments-QuantitativeSpeed. The implementation on the CPU using NEON *SIMD intrinsics with a *CT of size \(5\times 5\)  pixels achieves similar and, in case of the smaller image resolution, slightly better results than its GPU counterpart.
In summary, when considering the actual estimate, our configuration \(\text{*{CT}}_{9\times 7}\)  - *SGM executed on the GPU, with the original image size of \(1242\times 375\)  pixels, outperforms the baseline implementations as well as the other algorithms optimized for embedded hardware. When evaluating on the interpolated disparity maps, our approaches achieve similar and mostly better results than the other embedded algorithms. The superiority of the baseline algorithms from [1]} and [2]} with respect to the quality of the disparity estimation is to be expected, since they were optimized with respect to accuracy and not speed or throughput.
